Edge cloud platform for mission critical applications

ABSTRACT

A method implements a network slicing controller to manage network slicing instances in an edge cloud platform. The method includes receiving at least one policy change from an artificial intelligence powered smart traffic controller (APSTC) or an artificial intelligence powered edge traffic controller (APETC), determining whether the at least one policy change is valid based on local monitoring information, and sending the at least one policy change to a common control network function in a 5G mobile network.

TECHNICAL FIELD

Embodiments of the invention relate to the field of network management;and more specifically, to the improved management of network sliceinstances in a 5G mobile network.

BACKGROUND ART

Cellular or mobile communication networks (herein after referred to as‘mobile networks’) are widely utilized communication networks thatenable communication by user equipment (UE) via a wireless link with theremainder of the mobile network, other devices accessible via the mobilenetwork, and other connected networks. Mobile networks are distributedover large geographical areas. The components of the mobile networksthat interface with UE via the wireless communication are referred to as“cells,” each cell including at least one fixed-location transceiver,but more normally, a set of transceivers referred to as a basetransceiver station or base station. The base stations provide access toUEs within the cell to the mobile network, which can be used fortransmission of voice, data, and other types of content. Mobile networkoperators (MNOs) develop and maintain the mobile networks and contractwith subscribers to provide service to their respective UEs.

Mobile networks are based on evolving sets of technology to improve thequality of services and the throughput offered to UEs. An emergingtechnology is the 5^(th) Generation (5G) new radio (NR) technology asdefined by the 3^(rd) generation partnership project (3GPP). The 5Gmobile network includes a number of functions that can be distributedover any number and combination of electronic devices including theelectronic devices of a base station, radio access network (RAN), andother devices in the 5G mobile network core. In a 5G mobile network, aUE can be connected to the 5G mobile network via the RAN including anext generation node basestation (gNodeB) and similar components of theRAN. The RAN can include any number of gNodeBs that service any numberof UEs. Various functions can be distributed to partially or completelyexecute at gNodeBs or related components to reduce the latency betweenthe functions and the UEs. Computing services at the gNodeB or relatedcomponents can be managed as edge services or an edge cloud platform inconjunction with computing services elsewhere in the 5G mobile network.

SUMMARY

In one embodiment, a method implements a network slicing controller tomanage network slicing instances in an edge cloud platform. The methodincludes receiving at least one policy change from an artificialintelligence powered smart traffic controller (APSTC) or an artificialintelligence powered edge traffic controller (APETC), determiningwhether the at least one policy change is valid based on localmonitoring information, and sending the at least one policy change to acommon control network function in a 5G mobile network.

In another embodiment, a network device executes the method for anetwork slicing controller (NSC) to manage network slicing instances inan edge cloud platform. The network device includes a non-transitorycomputer-readable medium having stored therein a network slicingcontroller, and a processor coupled to the non-transitorycomputer-readable medium, the processor to execute the NSC, the NSC toreceive at least one policy change from an artificial intelligencepowered smart traffic controller (APSTC) or an artificial intelligencepowered edge traffic controller (APETC), to determine whether the atleast one policy change is valid based on local monitoring information,and to send the at least one policy change to a common control networkfunction in a 5G mobile network.

In one embodiment, a computing device executes a plurality of virtualmachines, the plurality of virtual machines implementing networkfunction virtualization (NFV), the plurality of virtual machines toexecute a method for a network slicing controller to manage networkslicing instances in an edge cloud platform. The computing deviceincludes a non-transitory computer-readable medium having stored thereina network slicing controller, and a processor coupled to thenon-transitory computer-readable medium, the processor to execute theplurality of virtual machines, at least one of the plurality of virtualmachines to execute the NSC, the NSC to receive at least one policychange from an artificial intelligence powered smart traffic controller(APSTC) or an artificial intelligence powered edge traffic controller(APETC), to determine whether the at least one policy change is validbased on local monitoring information, and to send the at least onepolicy change to a common control network function in a 5G mobilenetwork.

In one embodiment, a computing device executes a control plane of asoftware defined networking (SDN) network, the computing device toimplement a method for an artificial intelligence powered smart trafficcontroller (APSTC), the APSTC to manage network slicing instances in anedge cloud platform. The computing device includes a non-transitorycomputer-readable medium (848) having stored therein the APSTC, and aprocessor coupled to the non-transitory computer-readable medium, theprocessor to execute the APSTC, the APSTC to determine at least onepolicy change for managing network slicing instances in an edgecomputing platform (ECP) based on collected network metrics and anartificial intelligence or machine learning model, the APSTC to send theat least one policy change to a network slicing controller (NSC), and tocollect updated network metrics from an ECP edge data centerimplementing the NSC and the at least one policy change.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may best be understood by referring to the followingdescription and accompanying drawings that are used to illustrateembodiments of the invention. In the drawings:

FIG. 1 is a diagram of one example embodiment of a public networkconnecting a set of mobile networks with a central management station.

FIG. 2 is a diagram of one example embodiment of functions in a set ofmobile networks that support network slice instances.

FIG. 3 is a timing diagram of one embodiment of the operations tosupport artificial intelligence powered smart traffic controller (APSTC)management of NSIs.

FIG. 4 is a flowchart of one embodiment of the operations to supportartificial intelligence powered edge traffic controller (APETC)management of NSIs.

FIG. 5 is a flowchart of one embodiment of the operations of a networkslicing controller (NSC).

FIG. 6 is a flowchart of one embodiment of the operations of an APSTC.

FIG. 7 is a flowchart of one embodiment of the operations of an APETC.

FIG. 8A illustrates connectivity between network devices (NDs) within anexemplary network, as well as three exemplary implementations of theNDs, according to some embodiments of the invention.

FIG. 8B illustrates an exemplary way to implement a special-purposenetwork device according to some embodiments of the invention.

FIG. 8C illustrates various exemplary ways in which virtual networkelements (VNEs) may be coupled according to some embodiments of theinvention.

FIG. 8D illustrates a network with a single network element (NE) on eachof the NDs, and within this straight forward approach contrasts atraditional distributed approach (commonly used by traditional routers)with a centralized approach for maintaining reachability and forwardinginformation (also called network control), according to some embodimentsof the invention.

FIG. 8E illustrates the simple case of where each of the NDs implementsa single NE, but a centralized control plane has abstracted multiple ofthe NEs in different NDs into (to represent) a single NE in one of thevirtual network(s), according to some embodiments of the invention.

FIG. 8F illustrates a case where multiple VNEs are implemented ondifferent NDs and are coupled to each other, and where a centralizedcontrol plane has abstracted these multiple VNEs such that they appearas a single VNE within one of the virtual networks, according to someembodiments of the invention.

FIG. 9 illustrates a general purpose control plane device withcentralized control plane (CCP) software 950), according to someembodiments of the invention.

DETAILED DESCRIPTION

The following description describes methods and apparatus for networkslicing instance management in networks that combine mobile networks (5Gnetworks) and edge cloud networks. The embodiments provide improvedprocesses for the operation of a network slicing controller (NSC),artificial intelligence powered smart traffic controller (APSTC),artificial intelligence powered edge traffic controller (APETC), andsimilar components in a 5G mobile network and associated computingplatforms to improve usage of computing resources across an edgecomputing platform (ECP) while minimizing latency for services providedby the ECP. In the following description, numerous specific details suchas logic implementations, opcodes, means to specify operands, resourcepartitioning/sharing/duplication implementations, types andinterrelationships of system components, and logicpartitioning/integration choices are set forth in order to provide amore thorough understanding of the present invention. It will beappreciated, however, by one skilled in the art that the invention maybe practiced without such specific details. In other instances, controlstructures, gate level circuits and full software instruction sequenceshave not been shown in detail in order not to obscure the invention.Those of ordinary skill in the art, with the included descriptions, willbe able to implement appropriate functionality without undueexperimentation.

References in the specification to “one embodiment,” “an embodiment,”“an example embodiment,” etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to affect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

Bracketed text and blocks with dashed borders (e.g., large dashes, smalldashes, dot-dash, and dots) may be used herein to illustrate optionaloperations that add additional features to embodiments of the invention.However, such notation should not be taken to mean that these are theonly options or optional operations, and/or that blocks with solidborders are not optional in certain embodiments of the invention.

In the following description and claims, the terms “coupled” and“connected,” along with their derivatives, may be used. It should beunderstood that these terms are not intended as synonyms for each other.“Coupled” is used to indicate that two or more elements, which may ormay not be in direct physical or electrical contact with each other,co-operate or interact with each other. “Connected” is used to indicatethe establishment of communication between two or more elements that arecoupled with each other.

An electronic device stores and transmits (internally and/or with otherelectronic devices over a network) code (which is composed of softwareinstructions and which is sometimes referred to as computer program codeor a computer program) and/or data using machine-readable media (alsocalled computer-readable media), such as machine-readable storage media(e.g., magnetic disks, optical disks, solid state drives, read onlymemory (ROM), flash memory devices, phase change memory) andmachine-readable transmission media (also called a carrier) (e.g.,electrical, optical, radio, acoustical or other form of propagatedsignals—such as carrier waves, infrared signals). Thus, an electronicdevice (e.g., a computer) includes hardware and software, such as a setof one or more processors (e.g., wherein a processor is amicroprocessor, controller, microcontroller, central processing unit,digital signal processor, application specific integrated circuit, fieldprogrammable gate array, other electronic circuitry, a combination ofone or more of the preceding) coupled to one or more machine-readablestorage media to store code for execution on the set of processorsand/or to store data. For instance, an electronic device may includenon-volatile memory containing the code since the non-volatile memorycan persist code/data even when the electronic device is turned off(when power is removed), and while the electronic device is turned onthat part of the code that is to be executed by the processor(s) of thatelectronic device is typically copied from the slower non-volatilememory into volatile memory (e.g., dynamic random access memory (DRAM),static random access memory (SRAM)) of that electronic device. Typicalelectronic devices also include a set of one or more physical networkinterface(s) (NI(s)) to establish network connections (to transmitand/or receive code and/or data using propagating signals) with otherelectronic devices. For example, the set of physical NIs (or the set ofphysical NI(s) in combination with the set of processors executing code)may perform any formatting, coding, or translating to allow theelectronic device to send and receive data whether over a wired and/or awireless connection. In some embodiments, a physical NI may compriseradio circuitry capable of receiving data from other electronic devicesover a wireless connection and/or sending data out to other devices viaa wireless connection. This radio circuitry may include transmitter(s),receiver(s), and/or transceiver(s) suitable for radiofrequencycommunication. The radio circuitry may convert digital data into a radiosignal having the appropriate parameters (e.g., frequency, timing,channel, bandwidth, etc.). The radio signal may then be transmitted viaantennas to the appropriate recipient(s). In some embodiments, the setof physical NI(s) may comprise network interface controller(s) (NICs),also known as a network interface card, network adapter, or local areanetwork (LAN) adapter. The NIC(s) may facilitate in connecting theelectronic device to other electronic devices allowing them tocommunicate via wire through plugging in a cable to a physical portconnected to a NIC. One or more parts of an embodiment of the inventionmay be implemented using different combinations of software, firmware,and/or hardware.

A network device (ND) is an electronic device that communicativelyinterconnects other electronic devices on the network (e.g., othernetwork devices, end-user devices). Some network devices are “multipleservices network devices” that provide support for multiple networkingfunctions (e.g., routing, bridging, switching, Layer 2 aggregation,session border control, Quality of Service, and/or subscribermanagement), and/or provide support for multiple application services(e.g., data, voice, and video).

The embodiments provide improved operation for 5G mobile networks. In 5Gmobile network technology, a concept of network slice instance isintroduced. In the embodiments, a 5G mobile network technology providesa framework to set up and manage the network slice entity, which makes areservation of computing resources on computing devices includingnetwork devices to have a guaranteed network performance from the 5Gmobile network core to user equipment (UE) (i.e., mobile devices), wherethe performance encompasses guaranteed metrics such as latency andthroughput. For a mission critical or latency sensitive application, theinformation or instructions provided by applications or services are tobe delivered to UE (i.e., mobile devices) as quickly as possible. Theend to end latency from an application or service to an end user device(UE) is expected to be at the millisecond level.

In the current cloud platform architectures (e.g., such as thoseprovided by Amazon Web Services (AWS), Google Cloud Platform (GCP),Microsoft Azure or similar cloud platforms) a service producer candeploy its applications in the data center (e.g., such as using virtualmachines (VM), kubernetes (K8s) or similar technology) and provide itsservices towards its subscribers/end users through the 5G mobilenetwork.

However, current cloud platform architectures have several existingproblems. Since the connection between the data center and mobilenetworks goes through public/Internet networks, it is a ‘best effort’for the involved networks to deliver a mission criticalapplication/service to end users in terms of end-to-end latency. Thismeans that there is no guarantee for the end-to-end latency between theapplication/services and the UEs that are required by the missioncritical application/service. How to address this latency issue is achallenging problem. One approach to mitigate this problem is to get ridof public/Internet networks in the delivery path. This approach isreferred to as an “Edge Cloud Platform” (ECP), which is directlydeployed as a distributed data center inside a mobile network in orderto reduce the latency between the data center and the 5G mobile corenetwork (5G CN).

The architecture of the application/service delivery is provided througha mobile network operator. Within 5G mobile networks, there are twomajor components, 5G Core Network (5G CN) and 5G Radio Access Network(5G RAN). A 3GPP management system can set up the policies in those twocomponents, eventually applying the policies on the traffic nodes (TN)which route the data traffic on the data plane of the mobile network.However, this simple integration between the data center and 5G mobilenetwork is not flexible or sufficient to meet the demand from multiplemission critical or latency sensitive applications or services deployedin the data center or ECP. In particular, the current art is not able toutilize 5G mobile network resources efficiently. For instance, it mightover-allocate the network resources to deliver a mission criticalservice to single or multiple targeted end user groups at the beginning.But it cannot be adjusted based on the real data traffic from end users.

The embodiments overcome the limitations of the art to provide efficientnetwork usage for multiple mission critical applications in a 5G mobilenetwork.

FIG. 1 is a diagram of one example embodiment of a public networkconnecting a set of mobile networks with a central management station.The diagram illustrates the components of an ECP including a publicnetwork. The example ECP and network is provided by way of example andnot limitation. One skilled in the art would appreciate that the networkand ECP can encompass additional features, mobile networks, andcomponents. Many elements are omitted for sake of clarity andconciseness.

In the example embodiment, a ECP central management system (MS) managesboth a centralized datacenter (DC) (i.e., an ECP DC) and the associatedcomputing resources, as well as a set of ECP edge DCs and the associatedcomputing resources. The computing resources (x, y, z) of the ECP DC aredeployed adjacent to a public network (e.g., Internet) 103, where thecomputing resources can be any number and combination of processing andstorage resources in communication with the public network 103 and acentral ECP MS 101. A ‘set,’ as used herein refers to any positive wholenumber of items including one item.

The ECP edge DC and related resources are deployed within a serviceprovider (SP) network. Examples of SP networks include mobile networks105A and 105B, as well as, cable, fixed line, and similar networks 107.These SP networks can include data center resources including servers onwhich one or multiple mission critical applications are deployed. Thesecomputing resources can include servers, network devices, and similarcomputing resources. Any number and variety of computing resources canbe deployed as part of the ECP across the public network 103, SPnetworks, and similar locations.

An application deployed in the ECP DC footprint (e.g., application Y)provides a service to an end user group A through an associated Internetand Cable/fixed line operator network 107. For example, the group of enduser devices A can access the SP network 107 via Wi-Fi access followingpath (A). The same application can be deployed in the ECP edge DCfootprint (e.g., at E-J) provides the service to end user group Bthrough 5G mobile network 105B by following path (B).

FIG. 2 is a diagram of one example embodiment of functions in a set ofmobile networks that support network slice instances. The diagram ofFIG. 2 provides additional detail of the operations in SP networks(e.g., 105A and 105B) as well as at the ECP MS 101. The additionaldetail of this view of the network introduces a Network SlicingController (NSC) 201A 201B in the ECP edge DC, which can update thepolicy for the network slice instances (NSIs) 203 deployed in theexample 5G mobile networks 105A and 105B. The policies to be promulgatedto the NSIs 203 can based on the traffic pattern predicated byartificial intelligence/machine learning (AI/ML) powered models. TheAI/ML models can be trained by using network traffic samples collectedfrom ECP edge DC resources as well as metrics collected at the ECP DC.The ECP DC is not shown in FIG. 2 for simplicity, to focus on 5G mobilenetwork integration with ECP.

An APETC (AI Powered Edge Traffic Controller) 209 and APSTC (AI PoweredSmart Traffic Controller) 207 are involved in managing traffic policiesfor NSIs 203. The APETC 209 and APSTC 207 can be any AI or ML drivenmanagement systems for analyzing network metrics and deriving optimizednetwork policies for traffic management. The APETC 209 is deployed inthe ECP edge DC (i.e., in the footprint of an SP network such as mobilenetworks 105A and 105B). The APETC 209 provides “real time trafficprediction” based on the monitoring of the corresponding traffic relatedto single or multiple latency sensitive applications. The APETC 209provides the instructions to the NSC 201A and 201B tocreate/update/delete the corresponding policy for NSI in the 5G mobilenetwork if required.

The APSTC 207 controls components in ECP central MS 101. The APSTC 207collects network traffic information not only from a single SP network,but also can aggregate network traffic information from multiple SPnetworks (e.g., in a same region or globally). The APSTC 207 alsocollects the network traffic information from the ECP DC footprints,which can be deployed around the world but attached to the Internet orsimilar public network. The traffic data and metrics can be collected atthe ECP MS 101 by a data collection and analytics (DCA) component 211.An AI/ML algorithm, such as Time Series Deep Learning Algorithm, such asrecurrent neural network (RNN), transformer, long short term memory(LSTM), Reinforcement learning algorithm, or other machine learning orsimilar new technology, can be used to optimize the overall trafficrouting across different networks, locations and regions.

In cases where traffic optimization is required at specific SP networksor at a specific location within the SP network, the corresponding NSC201A, 201B in the ECP edge DC is notified about the update from thecentral MS 101 (e.g., originating at the APSTC 207 in some embodiments).The updated configuration for NSI policy is delivered to the NSC 201A,201B by the respective APETC 209. The NSC 201A, 201B, thencreates/updates/deletes the policies for the NSIs 203 under itsrespective management in the 5G mobile network 105A, 105B, such aspolicies that adjust latency or throughput of services provided by NSI203. Eventually, the SP 5G network management system willcreate/update/delete the resource allocations according to the updatedpolicies.

The improved ECP management of the embodiments, provides a variety ofadvantages over the art. The embodiments provide a smart mechanism todeploy mission critical applications in SP networks (e.g., 5G mobilenetworks). The embodiments utilize 5G mobile networks more efficientlythrough introduction and improved management of 5G network slicingtechnology. The improvements of the embodiments can attract more ECPtenants (application developers offering services via NSIs) to boost therevenue for mobile network operators. This in turn will increase thesale of 5G mobile network components including those that directlysupport the embodiments.

The embodiments also provide an alternative for 5G mobile networkoperators to benefit from the network slicing model implemented in 5Gnetwork infrastructures. The network operators can recover investmentsin 5G infrastructure and make a profit with a greater number of servicesbeing hosted by the ECP. End users also benefit from the proposedsolution to have good user experiences and reliable service with lowerlatencies and improved throughput.

The example embodiments of FIG. 2 further show three example SP networksand relevant components. Each of the SP networks including 5G mobilenetworks 105A and 105B, as well as fixed network 107 include ECP edge DCcomputing resources. These can be housed in a single location ordistributed over a corresponding geographic area. The 5G mobile networkseach include a 5G core network (CN), and a set of 5G radio accessnetworks (RANs). The user equipment and associated end users connect tothe respective 5G mobile network via local cells or base stations thatcan include ECP edge components. The set of 5G RANs in a mobile networkconnect with their respective 5G CN.

Network slice instances can utilize resources distributed across anycombination of the 5G CN and the set of 5G RANs for each 5G mobilenetwork. The network slice instances enable the use and combination ofvirtualized functions and independent logical networks on a single orshared physical network infrastructure. Each network slice instance canfunction as a separate or independent end-to-end network tailored tofulfil the requirements requested by a particular application.

For this reason, network slice instancing technology can play a centralrole to support 5G mobile networks that are designed to efficientlyembrace a plethora of services with very different service levelrequirements (SLR). The realization of network slicing instancingleverages the concepts of software defined networking (SDN) and networkfunction virtualization (NFV) that allow the implementation of flexibleand scalable network slice instances on top of a common networkinfrastructure.

FIG. 3 is a timing diagram of one embodiment of the operations tosupport artificial intelligence powered smart traffic controller (APSTC)management of NSIs. The flow of NSI policy updates via an APSTC in ECPcentral MS is illustrated. A general flow of how the APSTC operates tomanage the implementation of network traffic policies in a 5G mobilenetwork is illustrated. The process works with NSI in 5G mobile networkpolicies initiating with the APSTC and being distributed to NSI via anNSC at the ECP edge DC platform.

The process can be initiated by an APST based on predictions of trafficpattern made by an AI/ML process such that the APSTC decides to send (1)an update toward the NSC in an ECP edge DC. The policy can be sent as arequest for an NSC update (i.e., a create, read, update, delete (CRUD)).The NSC receives the request from the APSTC. This leads to an update ofthe NSI policy (e.g., to increase bandwidth or reduce the bandwidth).The NSC sends (2) the updated policy to a common control networkfunction (CCNF) in the 5G mobile network. Although in the illustratedexample, an update operation is shown, the process also supports andapplies to other CRUD operations.

The 5G CCNF does a validation on the request received from the NSC thatis deployed in the ECP edge DC associated with the 5G mobile network ofthe CCNF (3). The CCNF can accept a validated request. The 5G CCNF thensends the acceptance confirmation, e.g. a 201 Accepted, back to the NSCin the ECP edge DC (4). In cases where the policy or CRUD is notvalidated, then an error or similar indicator can be returned (notshown). The NSC in the ECP edge DC sends the confirmation, e.g. 201Accepted, back to APSTC (5).

The 5G CCNF adjusts the network resource for the NSI in the 5G networkcore (6) according to the CRUD. The 5G CCNF adjusts the network resourcefor NSI in 5G RAN (7) according to the CRUD. The NSI-Core sends aconfirmation back to 5G CCNF (8). Similarly, the NSI-RAN sends aconfirmation back to 5G CCNF (9).

When a given user equipment (e.g., UE-A) sends a request to theapplication deployed in ECP edge DC through NSI-RAN (10) to access thefunctions and data of the application, the updated policies for theapplication associated with the NSI are applied (11). The NSI-RANforwards the request toward the application associated with the NSI tothe 5G core (12). The 5G core similarly applies the updated policies forthe application and associated NSI while the request is being serviced(13). The request can be processed by the data collection point andlogged (e.g., an access of the application services by the UE is logged)(15). The request is forwarded to the application to be serviced (16).The application services the requests and sends a response back to thedata collection point (17) to be logged (e.g., the results from theapplication) (18), before the response is sent back to the UE via the 5Gnetwork core (19), 5G RAN (20), to end with the UE (21).

As a response from the application is forwarded to the UE, the datacollection point sends network metrics derived from collected datarelated to the communication between the application and the UE to thedata collection and analysis component of the ECP MS (22). The datacollection and analysis component acknowledges the received data (23).The data received from the ECP edge DC at the ECP central MS can besegregated based on the network operator of the associated 5G mobilenetwork to maintain proprietary data for each SP network. In otherembodiments, the collected data can be anonymized at the respective ECPedge DCs before being provided to the data collection and analysiscomponent (DCA) to enable greater data availability on networkconditions affecting policy decisions while maintaining control ofproprietary data at the SP network or similar entity. The anonymizeddata at the DCA can be made available to the ECP central MS for furtheranalysis. The collected data at the DCA component can be processed toidentify changes in network conditions that affect applicationsexecuting in the ECP edge DC or to organize the data as training samplesfor the APSTC (24). In this manner, the APSTC is able to perform aglobal analysis of data in the networks that it manages while keepingproprietary data in the respective SP networks. In the illustratedexample, the detected conditions are primarily related to latency.

The APSTC further analyzes the results of the compilation of collecteddata (e.g., receiving a latency report for an application) forapplications operating in the ECP edge DCs (25). Based on the receivedresults (26), the APSTC decides whether adjustments to resourceallotments and utilization in the ECP edge DC and 5G mobile networksupporting network slice instances are needed (26). The determination ofupdates to the policies can be made by application of any AI/MLalgorithm. The APSTC can apply the AI/ML algorithm to train a modelusing retrieved data samples from the DCA to build an updated predictionmodel. If the APSTC determines that a policy update is needed, then theprocess repeats the previously discussed steps to send the updatedpolicy for NSI (CRUD) (28) as an iterative process to continually adjustthe NSI policies at the NSC and 5G mobile network to balance resourcesfor NSI optimally (29-32).

In some embodiments, a single policy is set up for one application. Insome embodiments, one policy can be applied to different applications.If the same latency requirements are present for multiple applications,these applications can have a shared policy. The applications with ashared policy can be provided by single application provider ordifferent application providers.

In embodiments where shared policies are utilized, referred to as“coordination operations,” these coordination operations are recorded inthe dataset, which can also be used to train the AI/ML model of theAPSTC. Over time the model improves the intelligence and optimizationfor the coordination operations to work more efficiently.

For instance, two gaming applications can be deployed in the ECP. Onegaming application is provided by company A, the other by company B.Based on the prediction of the incoming traffic, the ECP central MSdeploys gaming application A in Toronto and Montreal, and gamingapplication B in Montreal and New York. Since the A and B gamingapplications are both serving at Montreal, a common policy can be usedfor both A and B gaming applications. The AI/ML model can create thecommon NSI policy for Rogers 5G network based on the anonymized datafrom the edge cloud in Montreal.

The AI/ML model builds the connections between A and B gaming bytraining the model using the experience for doing the policy combinationin the past, then apply these “experience” for the similar coordinationrequired in the future encountered traffic pattern.

The number of targeted SP networks managed by the ECP central MS canvary over time. The selection of targeted SP networks is made based onthe predicted traffic pattern from AI/ML trained model. The pool of SPnetworks and ECP DC footprints is dynamically formed based onpre-configurable criteria, such as regions or locations. It can also becompletely based on AI/ML traffic prediction model.

FIG. 4 is a flowchart of one embodiment of the operations to supportartificial intelligence powered edge traffic controller (APETC)management of NSIs. In this embodiment, the APETC manages policyadjustments for network slice instances. In the illustrated example, theprocess can be response to UE requests. In the illustrated example, UE-Asends a request to an application that is deployed in ECP edge DC (1).The network slice instance at the RAN applies the current trafficmanagement policy for the request to application (2). The network sliceinstance at the RAN forwards the request to the network slice instancefor the application in the 5G mobile network core (3).

The network slice instance in the 5G mobile network core applies thecurrent traffic management policies for the request to the application.The network slice instance in the 5G mobile network core forwards therequest to data collect point at the ECP edge DC (5). The data collectpoint logs the access information (6). The data collect point sends thedata for local analysis to the APETC (7). The APETC sends a confirmationback to data collect point (8) indicating the data has been receivedsuccessfully.

The data collect point forwards the request from the UE to thecorrelated application deployed in the ECP edge DC (9). The applicationprocesses the request and sends a response back to the data collectpoint (10). The data collect point logs the result of the application(i.e., the exit) (11). The data collect point sends the applicationresponse back to network slice instance in the 5G mobile network core(12). The network slice instance in the 5G mobile network core sends theresponse to the network slice instance in the RAN (13). The networkslice instance in the RAN sends the response back to UE (e.g., UE-A)(14).

The APETC does analysis for networking resource usage across differentapplications deployed in the ECP edge DC (15). The APETC sends a requestto the NSC to update the policy for the NSIs in the 5G mobile networkbased on the outcome of the analysis (16). The NSCs send the request toCCNF to update the policy for a given NSI in the 5G mobile network (17).The CCNF does a validation on the request from the NSC (18). The CCNFsends the confirmation back to NSC (19). The NSC sends the confirmationback to APETC (20).

In addition, the CCNF sends the request to NSI in the 5G mobile networkcore to update the policies for the NSI (CRUD) (21). The CCNF sends arequest to the NSI in the RAN to update the policy for the NSI (22). TheNSI in the 5G mobile network core sends a confirmation back to the CCNF(23). The NSI in the RAN sends the confirmation back to CCNF (24).

In this example, both UE-A and UE-B send requests to the application viaupdated NSI (referring to steps 25 to 36) to illustrate that the updatedpolicies can be applied to a variety of UEs making requests to the sameapplication. Any number of UEs can be serviced and the policiesdetermined by the APETC can be applied to all similarly situated UEs. Itis also possible that the APETC can act in a continuous iterativeprocess to update policies for resource management for NSIs byre-executing the equivalent to steps 7-22.

In this example, the update operation is used by way of illustration,the process also supports all CRUD operations.

The operations in the flow diagrams will be described with reference tothe exemplary embodiments of the other figures. However, it should beunderstood that the operations of the flow diagrams can be performed byembodiments of the invention other than those discussed with referenceto the other figures, and the embodiments of the invention discussedwith reference to these other figures can perform operations differentthan those discussed with reference to the flow diagrams.

FIG. 5 is a flowchart of one embodiment of the operations of a networkslicing controller (NSC). The operation of the NSC is provided by way ofexample and not limitation. One skilled in the art would understand thatthe functions described as being performed by the NSC can be performedby other components and that the NSC performs other operations notdescribed with relation to the illustration. The NSC can initiate theprocess in response to receiving a policy change (CRUD) from an APSTC orAPETC either directly or indirectly using any message format andcommunication medium (Block 501). The policy information can specifychanges to NSI resource allocation in a 5G mobile network and/or at anECP edge DC for any number and combination of applications with NSIs inan service provider network managed by the NSC. In the examples,discussion of a single set of policy changes for a given application isused by way of example. However, the set of policy changes can apply tomultiple applications and NSIs managed by the NSC at an ECP edge DC. Insome embodiments, the different applications are only viewed at the EPCcentral MS level where the application developers sign service levelagreement (SLA) with the edge cloud instead of SP. In this case, theylook like the same in the view of SP network (e.g., 5G mobile serviceprovider). In response to receiving the policy changes, the NSC sends anacknowledgement to the APSTC and/or APETC that originated the policychanges.

The NSC examines the updated policy changes to determine whether thepolicy changes for applications and NSIs managed by the NSC are validbased on local monitoring information (Block 505). For example, the NSCcan confirm that the associated applications are still running at theECP edge DC and/or using the NSIs in the 5G mobile network. If thepolicy changes are not valid, then the policy changes can be discarded(Block 507). In some embodiments, the NSC validation or a separatefunction can identify whether receive policy changes from the APSTCconflict with local policies set by an APETC or similar localmanagement. Depending on the configuration the local policies cansupersede or be superseded by the policy changes of the APSTC.Similarly, the updates of the APSTC can be out of date or alreadyimplemented local such that they are redundant. The NSC evaluates anddecides which policy changes to implement or validate. The policy changeupdate decision is logged locally at the ECP edge DC (e.g., in the datacollection point) (Block 509). The process then completes and the NSCawaits further policy updates from the APSTC or APETC.

If the updated policy changes are validated, then the NSC determines apriority for the policy change (Block 511). The priority can bedetermined based on assessment of how the policy is utilized byapplications of the ECP edge DC and mobile network. Policies that have aheavy usage or effect on applications are given a higher priority. Thepolicy update and priority information are recorded (e.g., by the datacollection point) (Block 513). The set of policies are then sent to theCCNF to be implemented by the 5G mobile network in priority order (Block515). Any number of policies affecting any number of NSIs andapplications can be processed as a group or ‘batch’ where theprioritization affects the order of implementation. The CCNF response toacknowledge receipt of the policy updates, which is logged (e.g., viathe data collection point) (Block 515). The NSC then awaits furtherpolicy updates from the APSTC or APETC.

FIG. 6 is a flowchart of one embodiment of the operations of an APSTC.In embodiments where the APSTC is managing NSIs across an ECP, theflowchart provides an example where policy updates are determined andsent to NSCs at ECP edge DCs for implementation. Examples describepolicy updates for a single NSC or ECP edge DC, however, one skilled inthe art would understand that a set of NSCs and/or ECP edge DCs can bemanaged as a group, based on separate SP networks, based on differentlocations, globally or in similar configurations. Data collected fromdifferent SP networks can be segregated for security or for protectionof proprietary interests. In some embodiments, data is anonymized toenable some degree of indirect sharing of information across SPnetworks.

An APSTC can continuously evaluate available network metrics todetermine at least one policy decision for at least one application inat least one location in the ECP (Block 601). The policy can bedetermined by any AI or ML algorithm or combination thereof. The set ofpolicies determined can be for any number of applications, NSCs, NSIs,ECP edge DCs, or similar components that support applications and theirservices in an ECP. For each policy that is updated based on the AI orML model that is generated by the APSTC, a set of policy changes aresent to the corresponding NSC (Block 603). The APSTC receives anacknowledgement from each NSC that the policy updates have been received(Block 605). If an acknowledgement is not received, the policy updatesmay be resent in some embodiments.

The APSTC in coordination with the DCA can continuously collect andreceive network metrics (e.g., key performance indicators (KPIs)) fromNSCs at various ECP edge DCs (Block 607). The received network metricinformation may be proprietary and can be separately maintained for eachSP network. In some embodiments, a copy of the received network metricscan be anonymized by removing service provider, UE, subscriber orsimilar information. In other embodiments, the data is anonymized at theEPC edge DC, before being provided to the DCA. The anonymized data canbe aggregated across SP networks to provide an improved, more detailed,and up to date data set for training and modeling the applicable AI/MLof the APSTC (Block 609). The aggregated data can then be analyzed toprepare a model for further updated policy changes (Block 611). The datacan be aggregated at differing levels and scope and models for thesedifferent scopes can be generated. The data and scope of the datacollection can include any one or more of an ECP edge DC, region,global, SP network, or similar scope. The embodiments also supportpolicy update (distribution) for single or multiple applications acrossdifferent locations in the ECP. The embodiments further supportcombining or merging the policy distribution for different applicationsat same location or different locations in the EPC. Similarly, theembodiments support removing or splitting the common policy fordifferent applications at the same location or different locations.These actions can be managed by the APSTC or similar components.

FIG. 7 is a flowchart of one embodiment of the operations of an APETC.In this example, the APETC operates to update policies local to an ECPedge DC, and/or SP network. Examples describe policy updates for asingle NSC or ECP edge DC, however, one skilled in the art wouldunderstand that a set of NSCs at an ECP edge DC can be managed as agroup, based on different locations, associated NSIs, or in similarconfigurations. An APETC can continuously evaluate available networkmetrics to determine at least one policy decision for at least oneapplication in the ECP edge DC (Block 701). The policy can be determinedby any AI or ML algorithm or combination thereof. The set of policiesdetermined can be for any number of applications, NSCs, NSIs, or similarcomponents that support applications and their services in an ECP edgeDC. For each policy that is updated based on the AI or ML model that isgenerated by the APETC, a set of policy changes are sent to thecorresponding local NSC (Block 703). The APETC receives anacknowledgement from each NSC that the policy updates have been received(Block 705). If an acknowledgement is not received, the policy updatesmay be resent in some embodiments.

The APETC in coordination with the DCA can continuously collect andreceive network metrics (e.g., KPIs) from NSCs at the local ECP edge DCs(Block 707). The received network metric information may be aggregatedfor analysis. All data that is aggregated can be analyzed as there isnot anonymized data at the local ECP edge DC, which enables the APETC togenerate more detailed local models and policies. The aggregated datacan then be analyzed to prepare a model for further updated policychanges (Block 709). The data can be aggregated at differing levels andscope and models for these different scopes can be generated. The dataand scope of the data collection can include any sub-division of theregion, resources, and components for an ECP edge DC and the associatedSP networks.

FIG. 8A illustrates connectivity between network devices (NDs) within anexemplary network, as well as three exemplary implementations of theNDs, according to some embodiments of the invention. FIG. 8A shows NDs800A-H, and their connectivity by way of lines between 800A-800B,800B-800C, 800C-800D, 800D-800E, 800E-800F, 800F-800G, and 800A-800G, aswell as between 800H and each of 800A, 800C, 800D, and 800G. These NDsare physical devices, and the connectivity between these NDs can bewireless or wired (often referred to as a link). An additional lineextending from NDs 800A, 800E, and 800F illustrates that these NDs actas ingress and egress points for the network (and thus, these NDs aresometimes referred to as edge NDs; while the other NDs may be calledcore NDs).

Two of the exemplary ND implementations in FIG. 8A are: 1) aspecial-purpose network device 802 that uses custom application-specificintegrated-circuits (ASICs) and a special-purpose operating system (OS);and 2) a general purpose network device 804 that uses commonoff-the-shelf (COTS) processors and a standard OS.

The special-purpose network device 802 includes networking hardware 810comprising a set of one or more processor(s) 812, forwarding resource(s)814 (which typically include one or more ASICs and/or networkprocessors), and physical network interfaces (NIs) 816 (through whichnetwork connections are made, such as those shown by the connectivitybetween NDs 800A-H), as well as non-transitory machine readable storagemedia 818 having stored therein networking software 820. Duringoperation, the networking software 820 may be executed by the networkinghardware 810 to instantiate a set of one or more networking softwareinstance(s) 822. Each of the networking software instance(s) 822, andthat part of the networking hardware 810 that executes that networksoftware instance (be it hardware dedicated to that networking softwareinstance and/or time slices of hardware temporally shared by thatnetworking software instance with others of the networking softwareinstance(s) 822), form a separate virtual network element 830A-R. Eachof the virtual network element(s) (VNEs) 830A-R includes a controlcommunication and configuration module 832A-R (sometimes referred to asa local control module or control communication module) and forwardingtable(s) 834A-R, such that a given virtual network element (e.g., 830A)includes the control communication and configuration module (e.g.,832A), a set of one or more forwarding table(s) (e.g., 834A), and thatportion of the networking hardware 810 that executes the virtual networkelement (e.g., 830A).

In some embodiments, the non-transitory machine-readable medium 818 canalso store the NSC/APETC/APSTC 865 or other components described herein.These components can be stored separately or in any combination withother components including the networking software 820. These componentscan be executed by the processors 812 of the special purpose networkdevice 802.

The special-purpose network device 802 is often physically and/orlogically considered to include: 1) a ND control plane 824 (sometimesreferred to as a control plane) comprising the processor(s) 812 thatexecute the control communication and configuration module(s) 832A-R;and 2) a ND forwarding plane 826 (sometimes referred to as a forwardingplane, a data plane, or a media plane) comprising the forwardingresource(s) 814 that utilize the forwarding table(s) 834A-R and thephysical NIs 816. By way of example, where the ND is a router (or isimplementing routing functionality), the ND control plane 824 (theprocessor(s) 812 executing the control communication and configurationmodule(s) 832A-R) is typically responsible for participating incontrolling how data (e.g., packets) is to be routed (e.g., the next hopfor the data and the outgoing physical NI for that data) and storingthat routing information in the forwarding table(s) 834A-R, and the NDforwarding plane 826 is responsible for receiving that data on thephysical NIs 816 and forwarding that data out the appropriate ones ofthe physical NIs 816 based on the forwarding table(s) 834A-R.

FIG. 8B illustrates an exemplary way to implement the special-purposenetwork device 802 according to some embodiments of the invention. FIG.8B shows a special-purpose network device including cards 838 (typicallyhot pluggable). While in some embodiments the cards 838 are of two types(one or more that operate as the ND forwarding plane 826 (sometimescalled line cards), and one or more that operate to implement the NDcontrol plane 824 (sometimes called control cards)), alternativeembodiments may combine functionality onto a single card and/or includeadditional card types (e.g., one additional type of card is called aservice card, resource card, or multi-application card). A service cardcan provide specialized processing (e.g., Layer 4 to Layer 7 services(e.g., firewall, Internet Protocol Security (IPsec), Secure SocketsLayer (SSL)/Transport Layer Security (TLS), Intrusion Detection System(IDS), peer-to-peer (P2P), Voice over IP (VoIP) Session BorderController, Mobile Wireless Gateways (Gateway General Packet RadioService (GPRS) Support Node (GGSN), Evolved Packet Core (EPC) Gateway)).By way of example, a service card may be used to terminate IPsec tunnelsand execute the attendant authentication and encryption algorithms.These cards are coupled together through one or more interconnectmechanisms illustrated as backplane 836 (e.g., a first full meshcoupling the line cards and a second full mesh coupling all of thecards).

Returning to FIG. 8A, the general purpose network device 804 includeshardware 840 comprising a set of one or more processor(s) 842 (which areoften COTS processors) and physical NIs 846, as well as non-transitorymachine readable storage media 848 having stored therein software 850.During operation, the processor(s) 842 execute the software 850 toinstantiate one or more sets of one or more applications 864A-R. Whileone embodiment does not implement virtualization, alternativeembodiments may use different forms of virtualization. For example, inone such alternative embodiment the virtualization layer 854 representsthe kernel of an operating system (or a shim executing on a baseoperating system) that allows for the creation of multiple instances862A-R called software containers that may each be used to execute one(or more) of the sets of applications 864A-R; where the multiplesoftware containers (also called virtualization engines, virtual privateservers, or jails) are user spaces (typically a virtual memory space)that are separate from each other and separate from the kernel space inwhich the operating system is run; and where the set of applicationsrunning in a given user space, unless explicitly allowed, cannot accessthe memory of the other processes. In another such alternativeembodiment the virtualization layer 854 represents a hypervisor(sometimes referred to as a virtual machine monitor (VMM)) or ahypervisor executing on top of a host operating system, and each of thesets of applications 864A-R is run on top of a guest operating systemwithin an instance 862A-R called a virtual machine (which may in somecases be considered a tightly isolated form of software container) thatis run on top of the hypervisor—the guest operating system andapplication may not know they are running on a virtual machine asopposed to running on a “bare metal” host electronic device, or throughpara-virtualization the operating system and/or application may be awareof the presence of virtualization for optimization purposes. In yetother alternative embodiments, one, some or all of the applications areimplemented as unikernel(s), which can be generated by compilingdirectly with an application only a limited set of libraries (e.g., froma library operating system (LibOS) including drivers/libraries of OSservices) that provide the particular OS services needed by theapplication. As a unikernel can be implemented to run directly onhardware 840, directly on a hypervisor (in which case the unikernel issometimes described as running within a LibOS virtual machine), or in asoftware container, embodiments can be implemented fully with unikernelsrunning directly on a hypervisor represented by virtualization layer854, unikernels running within software containers represented byinstances 862A-R, or as a combination of unikernels and theabove-described techniques (e.g., unikernels and virtual machines bothrun directly on a hypervisor, unikernels and sets of applications thatare run in different software containers).

In some embodiments, the non-transitory machine-readable medium 848 canalso store the NSC/APETC/APSTC 865 or other components described herein.These components can be stored separately or in any combination withother components including the software 850. These components can beexecuted by the processors 842 of the general purpose network device804.

The instantiation of the one or more sets of one or more applications864A-R, as well as virtualization if implemented, are collectivelyreferred to as software instance(s) 852. Each set of applications864A-R, corresponding virtualization construct (e.g., instance 862A-R)if implemented, and that part of the hardware 840 that executes them (beit hardware dedicated to that execution and/or time slices of hardwaretemporally shared), forms a separate virtual network element(s) 860A-R.

The virtual network element(s) 860A-R perform similar functionality tothe virtual network element(s) 830A-R—e.g., similar to the controlcommunication and configuration module(s) 832A and forwarding table(s)834A (this virtualization of the hardware 840 is sometimes referred toas network function virtualization (NFV)). Thus, NFV may be used toconsolidate many network equipment types onto industry standard highvolume server hardware, physical switches, and physical storage, whichcould be located in Data centers, NDs, and customer premise equipment(CPE). While embodiments of the invention are illustrated with eachinstance 862A-R corresponding to one VNE 860A-R, alternative embodimentsmay implement this correspondence at a finer level granularity (e.g.,line card virtual machines virtualize line cards, control card virtualmachine virtualize control cards, etc.); it should be understood thatthe techniques described herein with reference to a correspondence ofinstances 862A-R to VNEs also apply to embodiments where such a finerlevel of granularity and/or unikernels are used.

In certain embodiments, the virtualization layer 854 includes a virtualswitch that provides similar forwarding services as a physical Ethernetswitch. Specifically, this virtual switch forwards traffic betweeninstances 862A-R and the physical NI(s) 846, as well as optionallybetween the instances 862A-R; in addition, this virtual switch mayenforce network isolation between the VNEs 860A-R that by policy are notpermitted to communicate with each other (e.g., by honoring virtuallocal area networks (VLANs)).

The third exemplary ND implementation in FIG. 8A is a hybrid networkdevice 806, which includes both custom ASICs/special-purpose OS and COTSprocessors/standard OS in a single ND or a single card within an ND. Incertain embodiments of such a hybrid network device, a platform VM(i.e., a VM that that implements the functionality of thespecial-purpose network device 802) could provide forpara-virtualization to the networking hardware present in the hybridnetwork device 806.

Regardless of the above exemplary implementations of an ND, when asingle one of multiple VNEs implemented by an ND is being considered(e.g., only one of the VNEs is part of a given virtual network) or whereonly a single VNE is currently being implemented by an ND, the shortenedterm network element (NE) is sometimes used to refer to that VNE. Alsoin all of the above exemplary implementations, each of the VNEs (e.g.,VNE(s) 830A-R, VNEs 860A-R, and those in the hybrid network device 806)receives data on the physical NIs (e.g., 816, 846) and forwards thatdata out the appropriate ones of the physical NIs (e.g., 816, 846). Forexample, a VNE implementing IP router functionality forwards IP packetson the basis of some of the IP header information in the IP packet;where IP header information includes source IP address, destination IPaddress, source port, destination port (where “source port” and“destination port” refer herein to protocol ports, as opposed tophysical ports of a ND), transport protocol (e.g., user datagramprotocol (UDP), Transmission Control Protocol (TCP), and differentiatedservices code point (DSCP) values.

FIG. 8C illustrates various exemplary ways in which VNEs may be coupledaccording to some embodiments of the invention. FIG. 8C shows VNEs870A.1-870A.P (and optionally VNEs 870A.Q-870A.R) implemented in ND 800Aand VNE 870H.1 in ND 800H. In FIG. 8C, VNEs 870A.1-P are separate fromeach other in the sense that they can receive packets from outside ND800A and forward packets outside of ND 800A; VNE 870A.1 is coupled withVNE 870H.1, and thus they communicate packets between their respectiveNDs; VNE 870A.2-870A.3 may optionally forward packets between themselveswithout forwarding them outside of the ND 800A; and VNE 870A.P mayoptionally be the first in a chain of VNEs that includes VNE 870A.Qfollowed by VNE 870A.R (this is sometimes referred to as dynamic servicechaining, where each of the VNEs in the series of VNEs provides adifferent service—e.g., one or more layer 4-7 network services). WhileFIG. 8C illustrates various exemplary relationships between the VNEs,alternative embodiments may support other relationships (e.g.,more/fewer VNEs, more/fewer dynamic service chains, multiple differentdynamic service chains with some common VNEs and some different VNEs).

The NDs of FIG. 8A, for example, may form part of the Internet or aprivate network; and other electronic devices (not shown; such as enduser devices including workstations, laptops, netbooks, tablets, palmtops, mobile phones, smartphones, phablets, multimedia phones, VoiceOver Internet Protocol (VOIP) phones, terminals, portable media players,GPS units, wearable devices, gaming systems, set-top boxes, Internetenabled household appliances) may be coupled to the network (directly orthrough other networks such as access networks) to communicate over thenetwork (e.g., the Internet or virtual private networks (VPNs) overlaidon (e.g., tunneled through) the Internet) with each other (directly orthrough servers) and/or access content and/or services. Such contentand/or services are typically provided by one or more servers (notshown) belonging to a service/content provider or one or more end userdevices (not shown) participating in a peer-to-peer (P2P) service, andmay include, for example, public webpages (e.g., free content, storefronts, search services), private webpages (e.g., username/passwordaccessed webpages providing email services), and/or corporate networksover VPNs. For instance, end user devices may be coupled (e.g., throughcustomer premise equipment coupled to an access network (wired orwirelessly)) to edge NDs, which are coupled (e.g., through one or morecore NDs) to other edge NDs, which are coupled to electronic devicesacting as servers. However, through compute and storage virtualization,one or more of the electronic devices operating as the NDs in FIG. 8Amay also host one or more such servers (e.g., in the case of the generalpurpose network device 804, one or more of the software instances 862A-Rmay operate as servers; the same would be true for the hybrid networkdevice 806; in the case of the special-purpose network device 802, oneor more such servers could also be run on a virtualization layerexecuted by the processor(s) 812); in which case the servers are said tobe co-located with the VNEs of that ND.

A virtual network is a logical abstraction of a physical network (suchas that in FIG. 8A) that provides network services (e.g., L2 and/or L3services). A virtual network can be implemented as an overlay network(sometimes referred to as a network virtualization overlay) thatprovides network services (e.g., layer 2 (L2, data link layer) and/orlayer 3 (L3, network layer) services) over an underlay network (e.g., anL3 network, such as an Internet Protocol (IP) network that uses tunnels(e.g., generic routing encapsulation (GRE), layer 2 tunneling protocol(L2TP), IPSec) to create the overlay network).

A network virtualization edge (NVE) sits at the edge of the underlaynetwork and participates in implementing the network virtualization; thenetwork-facing side of the NVE uses the underlay network to tunnelframes to and from other NVEs; the outward-facing side of the NVE sendsand receives data to and from systems outside the network. A virtualnetwork instance (VNI) is a specific instance of a virtual network on aNVE (e.g., a NE/VNE on an ND, a part of a NE/VNE on a ND where thatNE/VNE is divided into multiple VNEs through emulation); one or moreVNIs can be instantiated on an NVE (e.g., as different VNEs on an ND). Avirtual access point (VAP) is a logical connection point on the NVE forconnecting external systems to a virtual network; a VAP can be physicalor virtual ports identified through logical interface identifiers (e.g.,a VLAN ID).

Examples of network services include: 1) an Ethernet LAN emulationservice (an Ethernet-based multipoint service similar to an InternetEngineering Task Force (IETF) Multiprotocol Label Switching (MPLS) orEthernet VPN (EVPN) service) in which external systems areinterconnected across the network by a LAN environment over the underlaynetwork (e.g., an NVE provides separate L2 VNIs (virtual switchinginstances) for different such virtual networks, and L3 (e.g., IP/MPLS)tunneling encapsulation across the underlay network); and 2) avirtualized IP forwarding service (similar to IETF IP VPN (e.g., BorderGateway Protocol (BGP)/MPLS IPVPN) from a service definitionperspective) in which external systems are interconnected across thenetwork by an L3 environment over the underlay network (e.g., an NVEprovides separate L3 VNIs (forwarding and routing instances) fordifferent such virtual networks, and L3 (e.g., IP/MPLS) tunnelingencapsulation across the underlay network)). Network services may alsoinclude quality of service capabilities (e.g., traffic classificationmarking, traffic conditioning and scheduling), security capabilities(e.g., filters to protect customer premises from network—originatedattacks, to avoid malformed route announcements), and managementcapabilities (e.g., full detection and processing).

FIG. 8D illustrates a network with a single network element on each ofthe NDs of FIG. 8A, and within this straight forward approach contrastsa traditional distributed approach (commonly used by traditionalrouters) with a centralized approach for maintaining reachability andforwarding information (also called network control), according to someembodiments of the invention. Specifically, FIG. 8D illustrates networkelements (NEs) 870A-H with the same connectivity as the NDs 800A-H ofFIG. 8A.

FIG. 8D illustrates that the distributed approach 872 distributesresponsibility for generating the reachability and forwardinginformation across the NEs 870A-H; in other words, the process ofneighbor discovery and topology discovery is distributed.

For example, where the special-purpose network device 802 is used, thecontrol communication and configuration module(s) 832A-R of the NDcontrol plane 824 typically include a reachability and forwardinginformation module to implement one or more routing protocols (e.g., anexterior gateway protocol such as Border Gateway Protocol (BGP),Interior Gateway Protocol(s) (IGP) (e.g., Open Shortest Path First(OSPF), Intermediate System to Intermediate System (IS-IS), RoutingInformation Protocol (RIP), Label Distribution Protocol (LDP), ResourceReservation Protocol (RSVP) (including RSVP-Traffic Engineering (TE):Extensions to RSVP for LSP Tunnels and Generalized Multi-Protocol LabelSwitching (GMPLS) Signaling RSVP-TE)) that communicate with other NEs toexchange routes, and then selects those routes based on one or morerouting metrics. Thus, the NEs 870A-H (e.g., the processor(s) 812executing the control communication and configuration module(s) 832A-R)perform their responsibility for participating in controlling how data(e.g., packets) is to be routed (e.g., the next hop for the data and theoutgoing physical NI for that data) by distributively determining thereachability within the network and calculating their respectiveforwarding information. Routes and adjacencies are stored in one or morerouting structures (e.g., Routing Information Base (RIB), LabelInformation Base (LIB), one or more adjacency structures) on the NDcontrol plane 824. The ND control plane 824 programs the ND forwardingplane 826 with information (e.g., adjacency and route information) basedon the routing structure(s). For example, the ND control plane 824programs the adjacency and route information into one or more forwardingtable(s) 834A-R (e.g., Forwarding Information Base (FIB), LabelForwarding Information Base (LFIB), and one or more adjacencystructures) on the ND forwarding plane 826. For layer 2 forwarding, theND can store one or more bridging tables that are used to forward databased on the layer 2 information in that data. While the above exampleuses the special-purpose network device 802, the same distributedapproach 872 can be implemented on the general purpose network device804 and the hybrid network device 806.

FIG. 8D illustrates that a centralized approach 874 (also known assoftware defined networking (SDN)) that decouples the system that makesdecisions about where traffic is sent from the underlying systems thatforwards traffic to the selected destination. The illustratedcentralized approach 874 has the responsibility for the generation ofreachability and forwarding information in a centralized control plane876 (sometimes referred to as a SDN control module, controller, networkcontroller, OpenFlow controller, SDN controller, control plane node,network virtualization authority, or management control entity), andthus the process of neighbor discovery and topology discovery iscentralized. The centralized control plane 876 has a south boundinterface 882 with a data plane 880 (sometime referred to theinfrastructure layer, network forwarding plane, or forwarding plane(which should not be confused with a ND forwarding plane)) that includesthe NEs 870A-H (sometimes referred to as switches, forwarding elements,data plane elements, or nodes). The centralized control plane 876includes a network controller 878, which includes a centralizedreachability and forwarding information module 879 that determines thereachability within the network and distributes the forwardinginformation to the NEs 870A-H of the data plane 880 over the south boundinterface 882 (which may use the OpenFlow protocol). Thus, the networkintelligence is centralized in the centralized control plane 876executing on electronic devices that are typically separate from theNDs.

In some embodiments, the centralized control plane 876 can alsoimplement the NSC/APETC/APSTC 881 or other components described herein.These components can be stored separately or in any combination withother components including the network controller 878. These componentscan be executed by processors of the centralized control plane 876.

For example, where the special-purpose network device 802 is used in thedata plane 880, each of the control communication and configurationmodule(s) 832A-R of the ND control plane 824 typically include a controlagent that provides the VNE side of the south bound interface 882. Inthis case, the ND control plane 824 (the processor(s) 812 executing thecontrol communication and configuration module(s) 832A-R) performs itsresponsibility for participating in controlling how data (e.g., packets)is to be routed (e.g., the next hop for the data and the outgoingphysical NI for that data) through the control agent communicating withthe centralized control plane 876 to receive the forwarding information(and in some cases, the reachability information) from the centralizedreachability and forwarding information module 879 (it should beunderstood that in some embodiments of the invention, the controlcommunication and configuration module(s) 832A-R, in addition tocommunicating with the centralized control plane 876, may also play somerole in determining reachability and/or calculating forwardinginformation—albeit less so than in the case of a distributed approach;such embodiments are generally considered to fall under the centralizedapproach 874, but may also be considered a hybrid approach).

While the above example uses the special-purpose network device 802, thesame centralized approach 874 can be implemented with the generalpurpose network device 804 (e.g., each of the VNE 860A-R performs itsresponsibility for controlling how data (e.g., packets) is to be routed(e.g., the next hop for the data and the outgoing physical NI for thatdata) by communicating with the centralized control plane 876 to receivethe forwarding information (and in some cases, the reachabilityinformation) from the centralized reachability and forwardinginformation module 879; it should be understood that in some embodimentsof the invention, the VNEs 860A-R, in addition to communicating with thecentralized control plane 876, may also play some role in determiningreachability and/or calculating forwarding information—albeit less sothan in the case of a distributed approach) and the hybrid networkdevice 806. In fact, the use of SDN techniques can enhance the NFVtechniques typically used in the general purpose network device 804 orhybrid network device 806 implementations as NFV is able to support SDNby providing an infrastructure upon which the SDN software can be run,and NFV and SDN both aim to make use of commodity server hardware andphysical switches.

FIG. 8D also shows that the centralized control plane 876 has a northbound interface 884 to an application layer 886, in which residesapplication(s) 888. The centralized control plane 876 has the ability toform virtual networks 892 (sometimes referred to as a logical forwardingplane, network services, or overlay networks (with the NEs 870A-H of thedata plane 880 being the underlay network)) for the application(s) 888.Thus, the centralized control plane 876 maintains a global view of allNDs and configured NEs/VNEs, and it maps the virtual networks to theunderlying NDs efficiently (including maintaining these mappings as thephysical network changes either through hardware (ND, link, or NDcomponent) failure, addition, or removal).

While FIG. 8D shows the distributed approach 872 separate from thecentralized approach 874, the effort of network control may bedistributed differently or the two combined in certain embodiments ofthe invention. For example: 1) embodiments may generally use thecentralized approach (SDN) 874, but have certain functions delegated tothe NEs (e.g., the distributed approach may be used to implement one ormore of fault monitoring, performance monitoring, protection switching,and primitives for neighbor and/or topology discovery); or 2)embodiments of the invention may perform neighbor discovery and topologydiscovery via both the centralized control plane and the distributedprotocols, and the results compared to raise exceptions where they donot agree. Such embodiments are generally considered to fall under thecentralized approach 874, but may also be considered a hybrid approach.

While FIG. 8D illustrates the simple case where each of the NDs 800A-Himplements a single NE 870A-H, it should be understood that the networkcontrol approaches described with reference to FIG. 8D also work fornetworks where one or more of the NDs 800A-H implement multiple VNEs(e.g., VNEs 830A-R, VNEs 860A-R, those in the hybrid network device806). Alternatively or in addition, the network controller 878 may alsoemulate the implementation of multiple VNEs in a single ND.Specifically, instead of (or in addition to) implementing multiple VNEsin a single ND, the network controller 878 may present theimplementation of a VNE/NE in a single ND as multiple VNEs in thevirtual networks 892 (all in the same one of the virtual network(s) 892,each in different ones of the virtual network(s) 892, or somecombination). For example, the network controller 878 may cause an ND toimplement a single VNE (a NE) in the underlay network, and thenlogically divide up the resources of that NE within the centralizedcontrol plane 876 to present different VNEs in the virtual network(s)892 (where these different VNEs in the overlay networks are sharing theresources of the single VNE/NE implementation on the ND in the underlaynetwork).

On the other hand, FIGS. 8E and 8F respectively illustrate exemplaryabstractions of NEs and VNEs that the network controller 878 may presentas part of different ones of the virtual networks 892. FIG. 8Eillustrates the simple case of where each of the NDs 800A-H implements asingle NE 870A-H (see FIG. 8D), but the centralized control plane 876has abstracted multiple of the NEs in different NDs (the NEs 870A-C andG-H) into (to represent) a single NE 8701 in one of the virtualnetwork(s) 892 of FIG. 8D, according to some embodiments of theinvention. FIG. 8E shows that in this virtual network, the NE 8701 iscoupled to NE 870D and 870F, which are both still coupled to NE 870E.

FIG. 8F illustrates a case where multiple VNEs (VNE 870A.1 and VNE870H.1) are implemented on different NDs (ND 800A and ND 800H) and arecoupled to each other, and where the centralized control plane 876 hasabstracted these multiple VNEs such that they appear as a single VNE870T within one of the virtual networks 892 of FIG. 8D, according tosome embodiments of the invention. Thus, the abstraction of a NE or VNEcan span multiple NDs.

While some embodiments of the invention implement the centralizedcontrol plane 876 as a single entity (e.g., a single instance ofsoftware running on a single electronic device), alternative embodimentsmay spread the functionality across multiple entities for redundancyand/or scalability purposes (e.g., multiple instances of softwarerunning on different electronic devices).

Similar to the network device implementations, the electronic device(s)running the centralized control plane 876, and thus the networkcontroller 878 including the centralized reachability and forwardinginformation module 879, may be implemented a variety of ways (e.g., aspecial purpose device, a general-purpose (e.g., COTS) device, or hybriddevice). These electronic device(s) would similarly includeprocessor(s), a set of one or more physical NIs, and a non-transitorymachine-readable storage medium having stored thereon the centralizedcontrol plane software. For instance, FIG. 9 illustrates, a generalpurpose control plane device 904 including hardware 940 comprising a setof one or more processor(s) 942 (which are often COTS processors) andphysical NIs 946, as well as non-transitory machine readable storagemedia 948 having stored therein centralized control plane (CCP) software950.

In some embodiments, the non-transitory machine-readable medium 948 canalso store the NSC/APETC/APSTC 981 or other components described herein.These components can be stored separately or in any combination withother components including the CCP software 950. These components can beexecuted by the processors 942 of the control plane device 904.

In embodiments that use compute virtualization, the processor(s) 942typically execute software to instantiate a virtualization layer 954(e.g., in one embodiment the virtualization layer 954 represents thekernel of an operating system (or a shim executing on a base operatingsystem) that allows for the creation of multiple instances 962A-R calledsoftware containers (representing separate user spaces and also calledvirtualization engines, virtual private servers, or jails) that may eachbe used to execute a set of one or more applications; in anotherembodiment the virtualization layer 954 represents a hypervisor(sometimes referred to as a virtual machine monitor (VMM)) or ahypervisor executing on top of a host operating system, and anapplication is run on top of a guest operating system within an instance962A-R called a virtual machine (which in some cases may be considered atightly isolated form of software container) that is run by thehypervisor; in another embodiment, an application is implemented as aunikernel, which can be generated by compiling directly with anapplication only a limited set of libraries (e.g., from a libraryoperating system (LibOS) including drivers/libraries of OS services)that provide the particular OS services needed by the application, andthe unikernel can run directly on hardware 940, directly on a hypervisorrepresented by virtualization layer 954 (in which case the unikernel issometimes described as running within a LibOS virtual machine), or in asoftware container represented by one of instances 962A-R). Again, inembodiments where compute virtualization is used, during operation aninstance of the CCP software 950 (illustrated as CCP instance 976A) isexecuted (e.g., within the instance 962A) on the virtualization layer954. In embodiments where compute virtualization is not used, the CCPinstance 976A is executed, as a unikernel or on top of a host operatingsystem, on the “bare metal” general purpose control plane device 904.The instantiation of the CCP instance 976A, as well as thevirtualization layer 954 and instances 962A-R if implemented, arecollectively referred to as software instance(s) 952.

In some embodiments, the CCP instance 976A includes a network controllerinstance 978. The network controller instance 978 includes a centralizedreachability and forwarding information module instance 979 (which is amiddleware layer providing the context of the network controller 878 tothe operating system and communicating with the various NEs), and an CCPapplication layer 980 (sometimes referred to as an application layer)over the middleware layer (providing the intelligence required forvarious network operations such as protocols, network situationalawareness, and user-interfaces). At a more abstract level, this CCPapplication layer 980 within the centralized control plane 876 workswith virtual network view(s) (logical view(s) of the network) and themiddleware layer provides the conversion from the virtual networks tothe physical view.

The centralized control plane 876 transmits relevant messages to thedata plane 880 based on CCP application layer 980 calculations andmiddleware layer mapping for each flow. A flow may be defined as a setof packets whose headers match a given pattern of bits; in this sense,traditional IP forwarding is also flow-based forwarding where the flowsare defined by the destination IP address for example; however, in otherimplementations, the given pattern of bits used for a flow definitionmay include more fields (e.g., 10 or more) in the packet headers.Different NDs/NEs/VNEs of the data plane 880 may receive differentmessages, and thus different forwarding information. The data plane 880processes these messages and programs the appropriate flow informationand corresponding actions in the forwarding tables (sometime referred toas flow tables) of the appropriate NE/VNEs, and then the NEs/VNEs mapincoming packets to flows represented in the forwarding tables andforward packets based on the matches in the forwarding tables.

Standards such as OpenFlow define the protocols used for the messages,as well as a model for processing the packets. The model for processingpackets includes header parsing, packet classification, and makingforwarding decisions. Header parsing describes how to interpret a packetbased upon a well-known set of protocols. Some protocol fields are usedto build a match structure (or key) that will be used in packetclassification (e.g., a first key field could be a source media accesscontrol (MAC) address, and a second key field could be a destination MACaddress).

Packet classification involves executing a lookup in memory to classifythe packet by determining which entry (also referred to as a forwardingtable entry or flow entry) in the forwarding tables best matches thepacket based upon the match structure, or key, of the forwarding tableentries. It is possible that many flows represented in the forwardingtable entries can correspond/match to a packet; in this case the systemis typically configured to determine one forwarding table entry from themany according to a defined scheme (e.g., selecting a first forwardingtable entry that is matched). Forwarding table entries include both aspecific set of match criteria (a set of values or wildcards, or anindication of what portions of a packet should be compared to aparticular value/values/wildcards, as defined by the matchingcapabilities—for specific fields in the packet header, or for some otherpacket content), and a set of one or more actions for the data plane totake on receiving a matching packet. For example, an action may be topush a header onto the packet, for the packet using a particular port,flood the packet, or simply drop the packet. Thus, a forwarding tableentry for IPv4/IPv6 packets with a particular transmission controlprotocol (TCP) destination port could contain an action specifying thatthese packets should be dropped.

Making forwarding decisions and performing actions occurs, based uponthe forwarding table entry identified during packet classification, byexecuting the set of actions identified in the matched forwarding tableentry on the packet.

However, when an unknown packet (for example, a “missed packet” or a“match-miss” as used in OpenFlow parlance) arrives at the data plane880, the packet (or a subset of the packet header and content) istypically forwarded to the centralized control plane 876. Thecentralized control plane 876 will then program forwarding table entriesinto the data plane 880 to accommodate packets belonging to the flow ofthe unknown packet. Once a specific forwarding table entry has beenprogrammed into the data plane 880 by the centralized control plane 876,the next packet with matching credentials will match that forwardingtable entry and take the set of actions associated with that matchedentry.

A network interface (NI) may be physical or virtual; and in the contextof IP, an interface address is an IP address assigned to a NI, be it aphysical NI or virtual NI. A virtual NI may be associated with aphysical NI, with another virtual interface, or stand on its own (e.g.,a loopback interface, a point-to-point protocol interface). A NI(physical or virtual) may be numbered (a NI with an IP address) orunnumbered (a NI without an IP address). A loopback interface (and itsloopback address) is a specific type of virtual NI (and IP address) of aNEVNE (physical or virtual) often used for management purposes; wheresuch an IP address is referred to as the nodal loopback address. The IPaddress(es) assigned to the NI(s) of a ND are referred to as IPaddresses of that ND; at a more granular level, the IP address(es)assigned to NI(s) assigned to a NEVNE implemented on a ND can bereferred to as IP addresses of that NEVNE.

While the invention has been described in terms of several embodiments,those skilled in the art will recognize that the invention is notlimited to the embodiments described, can be practiced with modificationand alteration within the spirit and scope of the appended claims. Thedescription is thus to be regarded as illustrative instead of limiting.

1. A method for a network slicing controller to manage network slicinginstances in an edge cloud platform, the method comprising: receiving atleast one policy change from an artificial intelligence powered smarttraffic controller (APSTC) or an artificial intelligence powered edgetraffic controller (APETC); determining whether the at least one policychange is valid based on local monitoring information; and sending theat least one policy change to a common control network function in a 5Gmobile network.
 2. The method of claim 1, further comprising: discardingthe at least one policy change in response to determining the at leastone policy change is invalid.
 3. The method of claim 1, furthercomprising: recording a validation decision on the at least one policychange in a data collection point in an edge cloud platform edge datacenter.
 4. The method of claim 1, further comprising: determining the atleast one policy change based on artificial intelligence or machinelearning algorithm analysis of network metrics collected for a networkslicing instance supporting an application in an edge cloud platformedge data center.
 5. The method of claim 1, further comprising:collecting network metrics from an edge computing platform edge datacenter for a service provider network; and analyzing the network metricsto generate an artificial intelligence or machine learning model toproduce the at least one policy change.
 6. A network device to execute amethod for a network slicing controller (NSC) to manage network slicinginstances in an edge cloud platform, the network device comprising: anon-transitory computer-readable medium having stored therein a networkslicing controller; and a processor coupled to the non-transitorycomputer-readable medium, the processor to execute the NSC, the NSC toreceive at least one policy change from an artificial intelligencepowered smart traffic controller (APSTC) or an artificial intelligencepowered edge traffic controller (APETC), to determine whether the atleast one policy change is valid based on local monitoring information,and to send the at least one policy change to a common control networkfunction in a 5G mobile network.
 7. The network device of claim 6,wherein the NSC is further discard the at least one policy change inresponse to determining the at least one policy change is invalid. 8.The network device of claim 6, wherein the NSC is further to record avalidation decision on the at least one policy change in a datacollection point in an edge cloud platform edge data center.
 9. Thenetwork device of claim 6, wherein the non-transitory computer-readablemedium stores the APETC, and wherein the APETC is further to determinethe at least one policy change based on artificial intelligence ormachine learning algorithm analysis of network metrics collected for anetwork slicing instance supporting an application in an edge cloudplatform edge data center.
 10. The network device of claim 6, whereinthe non-transitory computer-readable medium stores the APETC, andwherein the APETC is further to collect network metrics from an edgecomputing platform edge data center for a service provider network, andanalyze the network metrics to generate an artificial intelligence ormachine learning model to produce the at least one policy change.
 11. Acomputing device to execute a plurality of virtual machines, theplurality of virtual machines implementing network functionvirtualization (NFV), the plurality of virtual machines to execute amethod for a network slicing controller (NSC) to manage network slicinginstances in an edge cloud platform, the computing device comprising: anon-transitory computer-readable medium having stored therein a networkslicing controller; and a processor coupled to the non-transitorycomputer-readable medium, the processor to execute the plurality ofvirtual machines, at least one of the plurality of virtual machines toexecute the NSC, the NSC to receive at least one policy change from anartificial intelligence powered smart traffic controller (APSTC) or anartificial intelligence powered edge traffic controller (APETC), todetermine whether the at least one policy change is valid based on localmonitoring information, and to send the at least one policy change to acommon control network function in a 5G mobile network.
 12. Thecomputing device of claim 11, wherein the NSC is further discard the atleast one policy change in response to determining the at least onepolicy change is invalid.
 13. The computing device of claim 11, whereinthe NSC is further to record a validation decision on the at least onepolicy change in a data collection point in an edge cloud platform edgedata center.
 14. The computing device of claim 11, wherein thenon-transitory computer-readable medium stores the APETC, and whereinthe APETC is further to determine the at least one policy change basedon artificial intelligence or machine learning algorithm analysis ofnetwork metrics collected for a network slicing instance supporting anapplication in an edge cloud platform edge data center.
 15. Thecomputing device of claim 11, wherein the non-transitorycomputer-readable medium stores the APETC, and wherein the APETC isfurther to collect network metrics from an edge computing platform edgedata center for a service provider network, and analyze the networkmetrics to generate an artificial intelligence or machine learning modelto produce the at least one policy change.
 16. A computing device toexecute a control plane of a software defined networking (SDN) network,the computing device to implement a method for an artificialintelligence powered smart traffic controller (APSTC), the APSTC tomanage network slicing instances in an edge cloud platform, thecomputing device comprising: a non-transitory computer-readable mediumhaving stored therein the APSTC; and a processor coupled to thenon-transitory computer-readable medium, the processor to execute theAPSTC, the APSTC to determine at least one policy change for managingnetwork slicing instances in an edge computing platform based oncollected network metrics and an artificial intelligence or machinelearning model, the APSTC to send the at least one policy change to anetwork slicing controller (NSC), and to collect updated network metricsfrom an edge computing platform edge data center implementing the NSCand the at least one policy change.
 17. The computing device of claim16, wherein the APSTC is further configured to update the at least onepolicy for multiple applications across different locations, to combinepolicies for different applications at a same location or differentlocations in the edge computing platform, or to remove or split a commonpolicy for different applications at a same location or differentlocations.
 18. The computing device of claim 16, wherein APSTCanonymized data is collected from the edge computing platform edge datacenter.
 19. The computing device of claim 16, wherein the APSTCgenerates an artificial intelligence model or machine learning model fordiffering scopes including an edge computing platform edge data center,edge computing platform region, or service provider.
 20. The computingdevice of claim 16, wherein the APSTC manages policies in a plurality ofedge computing platform edge data centers via local network slicingcontrollers.